import torch
import math
from torch.optim.optimizer import Optimizer, required


class SGD(Optimizer):  #继承了父类 Optimizer
    r"""Implements stochastic gradient descent (optionally with momentum).

    Nesterov momentum is based on the formula from
    `On the importance of initialization and momentum in deep learning`__.

    Args:参数以字典的形式定义
        params (iterable): iterable of parameters to optimize or dicts defining
            parameter groups
        lr (float): learning rate  学习率
        momentum (float, optional): momentum factor (default: 0) 动量因子
        weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
        dampening (float, optional): dampening for momentum (default: 0)
        nesterov (bool, optional): enables Nesterov momentum (default: False)

    Example:
        >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9)
        >>> optimizer.zero_grad()
        >>> loss_fn(model(input), target).backward()
        >>> optimizer.step()

    __ http://www.cs.toronto.edu/%7Ehinton/absps/momentum.pdf

    .. note::
        The implementation of SGD with Momentum/Nesterov subtly differs from
        Sutskever et. al. and implementations in some other frameworks.

        Considering the specific case of Momentum, the update can be written as

        .. math::
                  v_{t+1} = \mu * v_{t} + g_{t+1} \\
                  p_{t+1} = p_{t} - lr * v_{t+1}

        where p, g, v and :math:`\mu` denote the parameters, gradient,
        velocity, and momentum respectively.

        This is in contrast to Sutskever et. al. and
        other frameworks which employ an update of the form

        .. math::
             v_{t+1} = \mu * v_{t} + lr * g_{t+1} \\
             p_{t+1} = p_{t} - v_{t+1}

        The Nesterov version is analogously modified.
    """

    def __init__(self, params, lr=required, momentum=0, dampening=0,
                 weight_decay=0, nesterov=False):
        #self.loss = loss
        if lr is not required and lr < 0.0:
            raise ValueError("Invalid learning rate: {}".format(lr))
        if momentum < 0.0:
            raise ValueError("Invalid momentum value: {}".format(momentum))
        if weight_decay < 0.0:
            raise ValueError("Invalid weight_decay value: {}".format(weight_decay))

        defaults = dict(lr=lr, momentum=momentum, dampening=dampening,
                        weight_decay=weight_decay, nesterov=nesterov)
        if nesterov and (momentum <= 0 or dampening != 0):
            raise ValueError("Nesterov momentum requires a momentum and zero dampening")
        super(SGD, self).__init__(params, defaults)

    def __setstate__(self, state):
        super(SGD, self).__setstate__(state)
        for group in self.param_groups:
            group.setdefault('nesterov', False)

    def step(self,cost, closure=None):
        """Performs a single optimization step.  更新所有参数

        Arguments:
            closure (callable, optional): A closure that reevaluates the model
                and returns the loss.
        """
        min_lr = 0.3
        max_lr = 0.9
        mylr = min_lr + (1-1/math.exp(cost))*(max_lr-min_lr)

        loss = None
        if closure is not None:
            loss = closure()
        #print('优化器中loss是', cost)
        for group in self.param_groups:

            

            group['lr'] = mylr
            weight_decay = group['weight_decay']
            momentum = group['momentum']
            dampening = group['dampening']
            nesterov = group['nesterov']

            for p in group['params']:  #对所有的权重和阈值进行更新
                if p.grad is None:  #p.grad 是参数p 的梯度 p是一个对象 grad是其属性
                    continue
                d_p = p.grad.data  #d_p 是梯度的值
                if weight_decay != 0:
                    d_p.add_(weight_decay, p.data)
                if momentum != 0:
                    param_state = self.state[p]
                    if 'momentum_buffer' not in param_state:
                        #print("执行一次，判断是否只是在初始时刻执行")
                        buf = param_state['momentum_buffer'] = torch.clone(d_p).detach()

                    else:
                        buf = param_state['momentum_buffer']
                        buf.mul_(momentum).add_(1 - dampening, d_p)

                    if nesterov:
                        d_p = d_p.add(momentum, buf)
                    else:
                        d_p = buf  #fou ze ,jiang donglaingfugei d_p
                
                p.data.add_(-group['lr'], d_p)    #随机梯度更新参数公式
        #print('优化器中的学习率是', group['lr'])
                
                # last_grad = temp_d_p
        return loss
